Modern lifestyle is undergoing one of the most fundamental changes in decades, thanks to quick emergence of a mobile computing paradigm. According to market statistics, smartphones and tablets have outsold personal computers for the first time in the fourth quarter of 2011. In the fourth quarter of 2014, unit shipment volumes of tablets alone are expected to exceed cumulative personal and business PC shipments, while by 2017, several forecasts anticipate about 87% of market share of smart connected devices to be smartphones and tablets and only 13% desktop and mobile PCs.
In response to growing volumes and expanding hardware capabilities and feature sets of smartphones and tablets, a new generation of mobile software applications utilizes enhanced connectivity of smartphones and takes advantage of their interaction with cloud services and client side computing power. Voice recognition, automatic question answering and other natural language processing technologies, using motion sensors and eye tracking for device and application control, text recognition in images, facial recognition and many more technology intense software applications, are changing the way people are using smartphones and communicating with each other and the world.
The next wave of mobile computing is broadly associated with multi-purpose and specialized mobile devices, especially wearable computers, such as smart glasses and other head-mounted cameras and displays, smart watches, wristware, etc. According to some forecasts, worldwide use of augmented reality devices, such as smart glasses, will reach 1% of the world population (over 70M units) by 2016.
Efficient techniques for identifying new contacts and capturing contact information during business and ad hoc meetings has long been viewed as one of the most prominent and challenging tasks of personal information management. Starting with basic procedures of writing down or typing each other's contact information and physical exchange of business cards, methods for capturing contact information have progressed to taking photographs of business cards or badges using smartphone cameras, followed by optical character recognition of captured images to retrieve at least partial contact information of a meeting participant (as implemented, for example, in the Page Camera feature of the EVERNOTE® cloud service and software for smartphones, developed by EVERNOTE® Corporation of Redwood City, Calif.).
With the emergence of social networks such as the LINKEDIN social network, the FACEBOOK social network or the TUMBLR social network that offer programmatic access to their databases by third parties, the next generation of contact capturing applications has evolved, exploiting a capability of automatic expansion of partial contact information obtained, for example, via a Page Camera feature, by scanning social networks, searching for the known partial people data and extracting additional details from the networks.
Other approaches employ automatic exchange of contact information between users residing in each other's proximity using various technologies: some applications may post contact information to a temporary network location so that it becomes available for an automatic retrieval by authorized users in a group; other applications use direct data transmission via NFC technologies.
While each of the above-listed automatic methods is noticeably more efficient than cataloging physical business cards or exchanging handwritten or hand-typed contact information, capturing business cards still requires multiple interactions between users and additional interaction steps by each user with a relevant smartphone software application and may run into logistical and mobility obstacles. As to direct transmission methods between new participants of a meeting, the direct transmission methods may not be sufficiently selective and may require establishing trusted connection between multiple devices, which also invites additional steps, such as exchanging pin codes typed on smartphones or other preliminary identification steps, which makes them comparable by complexity with typing in basic user information.
Recent developments in facial recognition and its growing accuracy may offer another opportunity to streamline contact identification tasks and turn contact information retrieval into a hands-free task. However, even high facial recognition accuracy does not guarantee a problem free solution when a system needs to identify a person from a large set of facial photographs, thus leading to potential accumulation of errors.
Accordingly, it is desirable to design a robust and streamlined system and workflow for retrieving contact information based on facial recognition.